Overview

Dataset statistics

Number of variables25
Number of observations33851
Missing cells139238
Missing cells (%)16.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.5 MiB
Average record size in memory200.0 B

Variable types

CAT14
NUM8
UNSUPPORTED2
BOOL1

Reproduction

Analysis started2021-04-22 19:46:45.081614
Analysis finished2021-04-22 19:47:03.233987
Duration18.15 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

position has constant value "0" Constant
created_at has constant value "1616604699" Constant
updated_at has constant value "1616604699" Constant
meta has constant value "{ }" Constant
Data as of has constant value "2021-03-24T00:00:00" Constant
State has a high cardinality: 54 distinct values High cardinality
Total Deaths is highly correlated with COVID-19 Deaths and 2 other fieldsHigh correlation
COVID-19 Deaths is highly correlated with Total Deaths and 3 other fieldsHigh correlation
Pneumonia Deaths is highly correlated with COVID-19 Deaths and 4 other fieldsHigh correlation
Pneumonia and COVID-19 Deaths is highly correlated with COVID-19 Deaths and 2 other fieldsHigh correlation
Influenza Deaths is highly correlated with Pneumonia Deaths and 1 other fieldsHigh correlation
Pneumonia, Influenza, or COVID-19 Deaths is highly correlated with COVID-19 Deaths and 4 other fieldsHigh correlation
End Date is highly correlated with Start Date and 1 other fieldsHigh correlation
Start Date is highly correlated with End Date and 1 other fieldsHigh correlation
Year is highly correlated with Start Date and 1 other fieldsHigh correlation
created_meta has 33851 (100.0%) missing values Missing
updated_meta has 33851 (100.0%) missing values Missing
Year has 4374 (12.9%) missing values Missing
Month has 13122 (38.8%) missing values Missing
COVID-19 Deaths has 6845 (20.2%) missing values Missing
Total Deaths has 5634 (16.6%) missing values Missing
Pneumonia Deaths has 8081 (23.9%) missing values Missing
Pneumonia and COVID-19 Deaths has 6311 (18.6%) missing values Missing
Influenza Deaths has 4789 (14.1%) missing values Missing
Pneumonia, Influenza, or COVID-19 Deaths has 7942 (23.5%) missing values Missing
Footnote has 14438 (42.7%) missing values Missing
COVID-19 Deaths is highly skewed (γ1 = 53.78029524) Skewed
Total Deaths is highly skewed (γ1 = 63.6556105) Skewed
Pneumonia Deaths is highly skewed (γ1 = 54.12139853) Skewed
Pneumonia and COVID-19 Deaths is highly skewed (γ1 = 53.10106605) Skewed
Influenza Deaths is highly skewed (γ1 = 57.09514985) Skewed
Pneumonia, Influenza, or COVID-19 Deaths is highly skewed (γ1 = 54.44722616) Skewed
Place of Death is uniformly distributed Uniform
Age group is uniformly distributed Uniform
sid has unique values Unique
id has unique values Unique
created_meta is an unsupported type, check if it needs cleaning or further analysis Unsupported
updated_meta is an unsupported type, check if it needs cleaning or further analysis Unsupported
HHS Region has 1458 (4.3%) zeros Zeros
COVID-19 Deaths has 16751 (49.5%) zeros Zeros
Total Deaths has 6318 (18.7%) zeros Zeros
Pneumonia Deaths has 14905 (44.0%) zeros Zeros
Pneumonia and COVID-19 Deaths has 19891 (58.8%) zeros Zeros
Influenza Deaths has 26864 (79.4%) zeros Zeros
Pneumonia, Influenza, or COVID-19 Deaths has 13417 (39.6%) zeros Zeros

Variables

sid
Categorical

UNIQUE

Distinct count33851
Unique (%)100.0%
Missing0
Missing (%)0.0%
Memory size264.5 KiB
row-ha2j~j77n_kcnh
 
1
row-vbfg~sfui.txxt
 
1
row-wed5.9v5f-c7va
 
1
row-ayu8.ecwi~wsiz
 
1
row-a3xt-k96p-6sdq
 
1
Other values (33846)
33846
ValueCountFrequency (%) 
row-ha2j~j77n_kcnh1< 0.1%
 
row-vbfg~sfui.txxt1< 0.1%
 
row-wed5.9v5f-c7va1< 0.1%
 
row-ayu8.ecwi~wsiz1< 0.1%
 
row-a3xt-k96p-6sdq1< 0.1%
 
row-fezt_y7f7_a5qx1< 0.1%
 
row-i7k2.ckda.gwje1< 0.1%
 
row-n4ha~98uv~rmxu1< 0.1%
 
row-8hzm-z8jt_qn5k1< 0.1%
 
row-bic6-er5s~j38e1< 0.1%
 
Other values (33841)33841> 99.9%
 
2021-04-22T20:47:03.367015image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length18
Median length18
Mean length18
Min length18

id
Categorical

UNIQUE

Distinct count33851
Unique (%)100.0%
Missing0
Missing (%)0.0%
Memory size264.5 KiB
00000000-0000-0000-9963-67838928CF7D
 
1
00000000-0000-0000-E69B-8700A48A3907
 
1
00000000-0000-0000-2508-30D3CF6350FF
 
1
00000000-0000-0000-D96A-736A2110A2B2
 
1
00000000-0000-0000-21F6-9B94E2EF0486
 
1
Other values (33846)
33846
ValueCountFrequency (%) 
00000000-0000-0000-9963-67838928CF7D1< 0.1%
 
00000000-0000-0000-E69B-8700A48A39071< 0.1%
 
00000000-0000-0000-2508-30D3CF6350FF1< 0.1%
 
00000000-0000-0000-D96A-736A2110A2B21< 0.1%
 
00000000-0000-0000-21F6-9B94E2EF04861< 0.1%
 
00000000-0000-0000-AC5D-0020828F7DD11< 0.1%
 
00000000-0000-0000-7CA2-A0D7D983ECBE1< 0.1%
 
00000000-0000-0000-9295-3E60A92D21B01< 0.1%
 
00000000-0000-0000-B1F2-8C376BFA5F451< 0.1%
 
00000000-0000-0000-4384-77D7488418791< 0.1%
 
Other values (33841)33841> 99.9%
 
2021-04-22T20:47:03.522050image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

position
Boolean

CONSTANT
REJECTED

Distinct count1
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size264.5 KiB
0
33851
ValueCountFrequency (%) 
033851100.0%
 

created_at
Categorical

CONSTANT
REJECTED

Distinct count1
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size264.5 KiB
1616604699
33851
ValueCountFrequency (%) 
161660469933851100.0%
 
2021-04-22T20:47:03.628076image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

created_meta
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing33851
Missing (%)100.0%
Memory size264.6 KiB

updated_at
Categorical

CONSTANT
REJECTED

Distinct count1
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size264.5 KiB
1616604699
33851
ValueCountFrequency (%) 
161660469933851100.0%
 
2021-04-22T20:47:03.733111image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

updated_meta
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing33851
Missing (%)100.0%
Memory size264.6 KiB

meta
Categorical

CONSTANT
REJECTED

Distinct count1
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size264.5 KiB
{ }
33851
ValueCountFrequency (%) 
{ }33851100.0%
 
2021-04-22T20:47:03.840130image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Data as of
Categorical

CONSTANT
REJECTED

Distinct count1
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size264.5 KiB
2021-03-24T00:00:00
33851
ValueCountFrequency (%) 
2021-03-24T00:00:0033851100.0%
 
2021-04-22T20:47:03.945165image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Start Date
Categorical

HIGH CORRELATION

Distinct count15
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size264.5 KiB
2020-01-01T00:00:00
10197
2021-01-01T00:00:00
5751
2020-08-01T00:00:00
 
1379
2020-07-01T00:00:00
 
1377
2020-10-01T00:00:00
 
1377
Other values (10)
13770
ValueCountFrequency (%) 
2020-01-01T00:00:001019730.1%
 
2021-01-01T00:00:00575117.0%
 
2020-08-01T00:00:0013794.1%
 
2020-07-01T00:00:0013774.1%
 
2020-10-01T00:00:0013774.1%
 
2021-03-01T00:00:0013774.1%
 
2020-02-01T00:00:0013774.1%
 
2020-05-01T00:00:0013774.1%
 
2020-09-01T00:00:0013774.1%
 
2020-11-01T00:00:0013774.1%
 
Other values (5)688520.3%
 
2021-04-22T20:47:04.051169image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

End Date
Categorical

HIGH CORRELATION

Distinct count15
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size264.5 KiB
2021-03-20T00:00:00
10125
2020-12-31T00:00:00
5751
2020-01-31T00:00:00
 
1449
2020-08-31T00:00:00
 
1379
2020-05-31T00:00:00
 
1377
Other values (10)
13770
ValueCountFrequency (%) 
2021-03-20T00:00:001012529.9%
 
2020-12-31T00:00:00575117.0%
 
2020-01-31T00:00:0014494.3%
 
2020-08-31T00:00:0013794.1%
 
2020-05-31T00:00:0013774.1%
 
2020-04-30T00:00:0013774.1%
 
2020-02-29T00:00:0013774.1%
 
2020-09-30T00:00:0013774.1%
 
2020-06-30T00:00:0013774.1%
 
2020-10-31T00:00:0013774.1%
 
Other values (5)688520.3%
 
2021-04-22T20:47:04.155192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Group
Categorical

Distinct count3
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size264.5 KiB
By Month
20729
By Year
8748
By Total
4374
ValueCountFrequency (%) 
By Month2072961.2%
 
By Year874825.8%
 
By Total437412.9%
 
2021-04-22T20:47:04.266217image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.741573366
Min length7

Year
Categorical

HIGH CORRELATION
MISSING

Distinct count2
Unique (%)< 0.1%
Missing4374
Missing (%)12.9%
Memory size264.5 KiB
2020
20972
2021
8505
ValueCountFrequency (%) 
20202097262.0%
 
2021850525.1%
 
(Missing)437412.9%
 
2021-04-22T20:47:04.372241image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.612360048
Min length3

Month
Real number (ℝ≥0)

MISSING

Distinct count12
Unique (%)0.1%
Missing13122
Missing (%)38.8%
Infinite0
Infinite (%)0.0%
Mean5.584253943750301
Minimum1.0
Maximum12.0
Zeros0
Zeros (%)0.0%
Memory size264.5 KiB
2021-04-22T20:47:04.463387image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.596528922
Coefficient of variation (CV)0.6440482396
Kurtosis-1.236584391
Mean5.584253944
Median Absolute Deviation (MAD)3
Skewness0.3390501768
Sum115756
Variance12.93502029
2021-04-22T20:47:04.534405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
128268.3%
 
327548.1%
 
227548.1%
 
813794.1%
 
713774.1%
 
1213774.1%
 
613774.1%
 
1013774.1%
 
1113774.1%
 
513774.1%
 
Other values (2)27548.1%
 
(Missing)1312238.8%
 
ValueCountFrequency (%) 
128268.3%
 
227548.1%
 
327548.1%
 
413774.1%
 
513774.1%
 
ValueCountFrequency (%) 
1213774.1%
 
1113774.1%
 
1013774.1%
 
913774.1%
 
813794.1%
 

HHS Region
Real number (ℝ≥0)

ZEROS

Distinct count11
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.47732710998198
Minimum0
Maximum10
Zeros1458
Zeros (%)4.3%
Memory size264.5 KiB
2021-04-22T20:47:04.619422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q38
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.956540698
Coefficient of variation (CV)0.5397780045
Kurtosis-1.082835947
Mean5.47732711
Median Absolute Deviation (MAD)2
Skewness-0.03489290778
Sum185413
Variance8.741132897
2021-04-22T20:47:04.695439image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
4558916.5%
 
9461713.6%
 
5389011.5%
 
3388811.5%
 
10340210.0%
 
126777.9%
 
826737.9%
 
624307.2%
 
722556.7%
 
014584.3%
 
ValueCountFrequency (%) 
014584.3%
 
126777.9%
 
29722.9%
 
3388811.5%
 
4558916.5%
 
ValueCountFrequency (%) 
10340210.0%
 
9461713.6%
 
826737.9%
 
722556.7%
 
624307.2%
 

State
Categorical

HIGH CARDINALITY

Distinct count54
Unique (%)0.2%
Missing0
Missing (%)0.0%
Memory size264.5 KiB
Delaware
 
1458
Florida
 
1458
Illinois
 
1458
Arizona
 
1458
Georgia
 
1458
Other values (49)
26561
ValueCountFrequency (%) 
Delaware14584.3%
 
Florida14584.3%
 
Illinois14584.3%
 
Arizona14584.3%
 
Georgia14584.3%
 
Indiana14584.3%
 
California14584.3%
 
Alaska14584.3%
 
Iowa14584.3%
 
Alabama14584.3%
 
Other values (44)1927156.9%
 
2021-04-22T20:47:04.846478image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length20
Median length8
Mean length8.548669168
Min length4

Place of Death
Categorical

UNIFORM

Distinct count9
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size264.5 KiB
Total - All Places of Death
3762
Healthcare setting, dead on arrival
3762
Hospice facility
3762
Decedent's home
3762
Nursing home/long term care facility
3762
Other values (4)
15041
ValueCountFrequency (%) 
Total - All Places of Death376211.1%
 
Healthcare setting, dead on arrival376211.1%
 
Hospice facility376211.1%
 
Decedent's home376211.1%
 
Nursing home/long term care facility376211.1%
 
Healthcare setting, inpatient376211.1%
 
Healthcare setting, outpatient or emergency room376211.1%
 
Other376011.1%
 
Place of death unknown375711.1%
 
2021-04-22T20:47:05.013513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length48
Median length27
Mean length25.89069747
Min length5

Age group
Categorical

UNIFORM

Distinct count9
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size264.5 KiB
40-49 years
3762
0-17 years
3762
18-29 years
3762
75-84 years
3761
50-64 years
3761
Other values (4)
15043
ValueCountFrequency (%) 
40-49 years376211.1%
 
0-17 years376211.1%
 
18-29 years376211.1%
 
75-84 years376111.1%
 
50-64 years376111.1%
 
65-74 years376111.1%
 
30-39 years376111.1%
 
All Ages376111.1%
 
85 years and over376011.1%
 
2021-04-22T20:47:05.133538image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length17
Median length11
Mean length11.2220023
Min length8

COVID-19 Deaths
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct count2058
Unique (%)7.6%
Missing6845
Missing (%)20.2%
Infinite0
Infinite (%)0.0%
Mean417.91983262978596
Minimum0.0
Maximum526027.0
Zeros16751
Zeros (%)49.5%
Memory size264.5 KiB
2021-04-22T20:47:05.211555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q336
95-th percentile990
Maximum526027
Range526027
Interquartile range (IQR)36

Descriptive statistics

Standard deviation5777.60144
Coefficient of variation (CV)13.82466442
Kurtosis3882.766281
Mean417.9198326
Median Absolute Deviation (MAD)0
Skewness53.78029524
Sum11286343
Variance33380678.4
2021-04-22T20:47:05.283572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
01675149.5%
 
112480.7%
 
102470.7%
 
132040.6%
 
122010.6%
 
141980.6%
 
161670.5%
 
151590.5%
 
171520.4%
 
181390.4%
 
Other values (2048)854025.2%
 
(Missing)684520.2%
 
ValueCountFrequency (%) 
01675149.5%
 
1700.2%
 
2540.2%
 
3400.1%
 
4270.1%
 
ValueCountFrequency (%) 
5260271< 0.1%
 
3790301< 0.1%
 
3422591< 0.1%
 
2410701< 0.1%
 
1625831< 0.1%
 

Total Deaths
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct count4927
Unique (%)17.5%
Missing5634
Missing (%)16.6%
Infinite0
Infinite (%)0.0%
Mean3079.5927632278413
Minimum0.0
Maximum4035809.0
Zeros6318
Zeros (%)18.7%
Memory size264.5 KiB
2021-04-22T20:47:05.370591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112
median84
Q3594
95-th percentile8180.6
Maximum4035809
Range4035809
Interquartile range (IQR)582

Descriptive statistics

Standard deviation40345.29074
Coefficient of variation (CV)13.10085256
Kurtosis5442.237732
Mean3079.592763
Median Absolute Deviation (MAD)84
Skewness63.6556105
Sum86896869
Variance1627742485
2021-04-22T20:47:05.454852image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0631818.7%
 
103010.9%
 
112580.8%
 
122560.8%
 
152140.6%
 
132120.6%
 
142060.6%
 
161770.5%
 
171690.5%
 
191680.5%
 
Other values (4917)1993858.9%
 
(Missing)563416.6%
 
ValueCountFrequency (%) 
0631818.7%
 
110< 0.1%
 
28< 0.1%
 
311< 0.1%
 
410< 0.1%
 
ValueCountFrequency (%) 
40358091< 0.1%
 
33664801< 0.1%
 
13393391< 0.1%
 
12570531< 0.1%
 
12082361< 0.1%
 

Pneumonia Deaths
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct count1911
Unique (%)7.4%
Missing8081
Missing (%)23.9%
Infinite0
Infinite (%)0.0%
Mean379.62173069460613
Minimum0.0
Maximum453484.0
Zeros14905
Zeros (%)44.0%
Memory size264.5 KiB
2021-04-22T20:47:05.551869image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q342
95-th percentile919.55
Maximum453484
Range453484
Interquartile range (IQR)42

Descriptive statistics

Standard deviation5275.495561
Coefficient of variation (CV)13.89671648
Kurtosis3762.484959
Mean379.6217307
Median Absolute Deviation (MAD)0
Skewness54.12139853
Sum9782852
Variance27830853.42
2021-04-22T20:47:05.621886image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
01490544.0%
 
103311.0%
 
112870.8%
 
132380.7%
 
122360.7%
 
142060.6%
 
151910.6%
 
161890.6%
 
191820.5%
 
181810.5%
 
Other values (1901)882426.1%
 
(Missing)808123.9%
 
ValueCountFrequency (%) 
01490544.0%
 
1640.2%
 
2570.2%
 
3380.1%
 
4300.1%
 
ValueCountFrequency (%) 
4534841< 0.1%
 
3484591< 0.1%
 
3365251< 0.1%
 
2538301< 0.1%
 
1280211< 0.1%
 

Pneumonia and COVID-19 Deaths
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct count1452
Unique (%)5.3%
Missing6311
Missing (%)18.6%
Infinite0
Infinite (%)0.0%
Mean200.31931735657227
Minimum0.0
Maximum255848.0
Zeros19891
Zeros (%)58.8%
Memory size264.5 KiB
2021-04-22T20:47:05.701902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312
95-th percentile422
Maximum255848
Range255848
Interquartile range (IQR)12

Descriptive statistics

Standard deviation2948.023364
Coefficient of variation (CV)14.71662046
Kurtosis3691.495411
Mean200.3193174
Median Absolute Deviation (MAD)0
Skewness53.10106605
Sum5516794
Variance8690841.755
2021-04-22T20:47:05.772911image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
01989158.8%
 
102110.6%
 
122040.6%
 
111800.5%
 
131680.5%
 
141530.5%
 
161280.4%
 
151230.4%
 
191160.3%
 
171140.3%
 
Other values (1442)625218.5%
 
(Missing)631118.6%
 
ValueCountFrequency (%) 
01989158.8%
 
11020.3%
 
2560.2%
 
3470.1%
 
4250.1%
 
ValueCountFrequency (%) 
2558481< 0.1%
 
2069271< 0.1%
 
1773201< 0.1%
 
1419961< 0.1%
 
785281< 0.1%
 

Influenza Deaths
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct count288
Unique (%)1.0%
Missing4789
Missing (%)14.1%
Infinite0
Infinite (%)0.0%
Mean6.182471956506778
Minimum0.0
Maximum9004.0
Zeros26864
Zeros (%)79.4%
Memory size264.5 KiB
2021-04-22T20:47:05.853935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile13
Maximum9004
Range9004
Interquartile range (IQR)0

Descriptive statistics

Standard deviation104.6396602
Coefficient of variation (CV)16.92521389
Kurtosis4219.748401
Mean6.182471957
Median Absolute Deviation (MAD)0
Skewness57.09514985
Sum179675
Variance10949.4585
2021-04-22T20:47:05.930953image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
02686479.4%
 
11460.4%
 
101060.3%
 
11960.3%
 
12880.3%
 
2880.3%
 
13610.2%
 
15580.2%
 
14540.2%
 
17530.2%
 
Other values (278)14484.3%
 
(Missing)478914.1%
 
ValueCountFrequency (%) 
02686479.4%
 
11460.4%
 
2880.3%
 
3450.1%
 
4330.1%
 
ValueCountFrequency (%) 
90041< 0.1%
 
87761< 0.1%
 
56861< 0.1%
 
55341< 0.1%
 
24361< 0.1%
 

Pneumonia, Influenza, or COVID-19 Deaths
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct count2359
Unique (%)9.1%
Missing7942
Missing (%)23.5%
Infinite0
Infinite (%)0.0%
Mean606.8093712609518
Minimum0.0
Maximum731429.0
Zeros13417
Zeros (%)39.6%
Memory size264.5 KiB
2021-04-22T20:47:06.227007image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q371
95-th percentile1472.6
Maximum731429
Range731429
Interquartile range (IQR)71

Descriptive statistics

Standard deviation8211.524317
Coefficient of variation (CV)13.53229648
Kurtosis3897.467243
Mean606.8093713
Median Absolute Deviation (MAD)0
Skewness54.44722616
Sum15721824
Variance67429131.6
2021-04-22T20:47:06.311026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
01341739.6%
 
103180.9%
 
112940.9%
 
122480.7%
 
132390.7%
 
142360.7%
 
151960.6%
 
161880.6%
 
181830.5%
 
171760.5%
 
Other values (2349)1041430.8%
 
(Missing)794223.5%
 
ValueCountFrequency (%) 
01341739.6%
 
1630.2%
 
2410.1%
 
3280.1%
 
4290.1%
 
ValueCountFrequency (%) 
7314291< 0.1%
 
5577981< 0.1%
 
4772431< 0.1%
 
3582061< 0.1%
 
2246411< 0.1%
 

Footnote
Categorical

MISSING

Distinct count1
Unique (%)< 0.1%
Missing14438
Missing (%)42.7%
Memory size264.5 KiB
One or more data cells have counts between 1-9 and have been suppressed in accordance with NCHS confidentiality standards.
19413
ValueCountFrequency (%) 
One or more data cells have counts between 1-9 and have been suppressed in accordance with NCHS confidentiality standards.1941357.3%
 
(Missing)1443842.7%
 
2021-04-22T20:47:06.426070image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length122
Median length122
Mean length71.2445718
Min length3

Interactions

2021-04-22T20:46:53.599223image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:53.716264image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:53.824291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:53.934311image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:54.053339image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:54.166364image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:54.277395image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:54.385416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:54.498438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:54.614470image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:54.735501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:54.847517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:54.970548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:55.081570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:55.195603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:55.304621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:55.576691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:55.688708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:55.800734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:55.914762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:56.040787image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:56.152814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:56.266840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:56.376867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:56.499898image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:56.621918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:56.747944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:56.877979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:57.017011image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:57.145036image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:57.275068image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:57.398095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:57.538127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:57.656154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:57.767162image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:57.881203image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:58.002215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:58.113242image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:58.223266image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:58.329290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:58.447316image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:58.561343image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:58.680369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:58.795419image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:58.921446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:59.034463image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:59.151496image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:59.263514image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:59.387541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:59.499571image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:59.604603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:59.713612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:59.832641image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:46:59.937665image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:47:00.055681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:47:00.159720image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:47:00.275742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:47:00.547808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:47:00.670836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:47:00.798415image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:47:00.936450image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:47:01.061478image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:47:01.187508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:47:01.314524image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-04-22T20:47:06.537723image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-04-22T20:47:06.774215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-04-22T20:47:07.011269image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-04-22T20:47:07.253323image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-04-22T20:47:07.472372image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-04-22T20:47:01.634589image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:47:02.290752image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:47:02.708377image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-22T20:47:02.999455image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

sididpositioncreated_atcreated_metaupdated_atupdated_metametaData as ofStart DateEnd DateGroupYearMonthHHS RegionStatePlace of DeathAge groupCOVID-19 DeathsTotal DeathsPneumonia DeathsPneumonia and COVID-19 DeathsInfluenza DeathsPneumonia, Influenza, or COVID-19 DeathsFootnote
0row-xrtt.u63m-petw00000000-0000-0000-985B-3AC768A0E7E101616604699NaN1616604699NaN{ }2021-03-24T00:00:002020-01-01T00:00:002021-03-20T00:00:00By TotalNaNNaN0United StatesTotal - All Places of DeathAll Ages526027.04035809.0453484.0255848.09004.0731429.0NaN
1row-xvvt_qzkw-rvt200000000-0000-0000-FD08-DDB30B29C9A901616604699NaN1616604699NaN{ }2021-03-24T00:00:002020-01-01T00:00:002021-03-20T00:00:00By TotalNaNNaN0United StatesTotal - All Places of Death0-17 years238.038250.0646.044.0179.01019.0NaN
2row-s9xs~pfcz_s4we00000000-0000-0000-DA88-303EA3BF193001616604699NaN1616604699NaN{ }2021-03-24T00:00:002020-01-01T00:00:002021-03-20T00:00:00By TotalNaNNaN0United StatesTotal - All Places of Death18-29 years1916.072834.02109.0850.0150.03313.0NaN
3row-rjn9~8pz5_tcjq00000000-0000-0000-FA1F-B0B6C8B6BC1C01616604699NaN1616604699NaN{ }2021-03-24T00:00:002020-01-01T00:00:002021-03-20T00:00:00By TotalNaNNaN0United StatesTotal - All Places of Death30-39 years5583.0103647.05088.02561.0318.08406.0NaN
4row-2ktj.5dff.a4re00000000-0000-0000-E7E4-D4897A0EF3A501616604699NaN1616604699NaN{ }2021-03-24T00:00:002020-01-01T00:00:002021-03-20T00:00:00By TotalNaNNaN0United StatesTotal - All Places of Death40-49 years15134.0156430.012934.07445.0494.021048.0NaN
5row-rjzn~8uab.sdrb00000000-0000-0000-DE94-04F2628A675B01616604699NaN1616604699NaN{ }2021-03-24T00:00:002020-01-01T00:00:002021-03-20T00:00:00By TotalNaNNaN0United StatesTotal - All Places of Death50-64 years78883.0659981.072258.041686.02128.0111258.0NaN
6row-emcy~xkxq_hr9z00000000-0000-0000-84DA-BC7EBB6B6B4C01616604699NaN1616604699NaN{ }2021-03-24T00:00:002020-01-01T00:00:002021-03-20T00:00:00By TotalNaNNaN0United StatesTotal - All Places of Death65-74 years115381.0810095.0104453.061572.01939.0159891.0NaN
7row-k4ns~4hrc_4xd600000000-0000-0000-EC33-3C1DCF81CEF601616604699NaN1616604699NaN{ }2021-03-24T00:00:002020-01-01T00:00:002021-03-20T00:00:00By TotalNaNNaN0United StatesTotal - All Places of Death75-84 years146309.0986336.0127975.074117.01955.0201853.0NaN
8row-wwyk.emea-2wei00000000-0000-0000-9333-A435A0084F3601616604699NaN1616604699NaN{ }2021-03-24T00:00:002020-01-01T00:00:002021-03-20T00:00:00By TotalNaNNaN0United StatesTotal - All Places of Death85 years and over162583.01208236.0128021.067573.01841.0224641.0NaN
9row-cy3u.a6nx.wtdf00000000-0000-0000-D713-E4051964DE7001616604699NaN1616604699NaN{ }2021-03-24T00:00:002020-01-01T00:00:002021-03-20T00:00:00By TotalNaNNaN0United StatesHealthcare setting, inpatientAll Ages342259.01257053.0336525.0206927.05686.0477243.0NaN

Last rows

sididpositioncreated_atcreated_metaupdated_atupdated_metametaData as ofStart DateEnd DateGroupYearMonthHHS RegionStatePlace of DeathAge groupCOVID-19 DeathsTotal DeathsPneumonia DeathsPneumonia and COVID-19 DeathsInfluenza DeathsPneumonia, Influenza, or COVID-19 DeathsFootnote
33841row-wy3r-htzw-wvu700000000-0000-0000-AD85-C10FB86FC6AF01616604699NaN1616604699NaN{ }2021-03-24T00:00:002020-01-01T00:00:002020-01-31T00:00:00By Month2020.01.07KansasOther0-17 years0.0NaN0.00.00.00.0One or more data cells have counts between 1-9 and have been suppressed in accordance with NCHS confidentiality standards.
33842row-kv4e_6kz5-a9gi00000000-0000-0000-055E-F7A56C74BF4401616604699NaN1616604699NaN{ }2021-03-24T00:00:002020-01-01T00:00:002020-01-31T00:00:00By Month2020.01.07KansasOther18-29 years0.016.00.00.00.00.0NaN
33843row-a54n_pyf6.t2s700000000-0000-0000-4E32-E93E22005AB201616604699NaN1616604699NaN{ }2021-03-24T00:00:002020-01-01T00:00:002020-01-31T00:00:00By Month2020.01.07KansasOther30-39 years0.011.0NaN0.00.0NaNOne or more data cells have counts between 1-9 and have been suppressed in accordance with NCHS confidentiality standards.
33844row-apdu_mjtw-yh4k00000000-0000-0000-8E7D-EE687DEF6FC601616604699NaN1616604699NaN{ }2021-03-24T00:00:002020-01-01T00:00:002020-01-31T00:00:00By Month2020.01.07KansasOther40-49 years0.011.00.00.00.00.0NaN
33845row-d5ym_ftah~gxsp00000000-0000-0000-CEE6-6E5523D292CD01616604699NaN1616604699NaN{ }2021-03-24T00:00:002020-01-01T00:00:002020-01-31T00:00:00By Month2020.01.01VermontPlace of death unknown40-49 years0.00.00.00.00.00.0NaN
33846row-qn8c~haza.hz4s00000000-0000-0000-DFA3-6C88E54F95C601616604699NaN1616604699NaN{ }2021-03-24T00:00:002020-01-01T00:00:002020-01-31T00:00:00By Month2020.01.01VermontPlace of death unknown50-64 years0.00.00.00.00.00.0NaN
33847row-45fu_6uq9.d3af00000000-0000-0000-0D4F-473835A443BD01616604699NaN1616604699NaN{ }2021-03-24T00:00:002020-01-01T00:00:002020-01-31T00:00:00By Month2020.01.01VermontPlace of death unknown65-74 years0.00.00.00.00.00.0NaN
33848row-9x5n~nept_enyu00000000-0000-0000-D817-2866AEF74D5D01616604699NaN1616604699NaN{ }2021-03-24T00:00:002020-01-01T00:00:002020-01-31T00:00:00By Month2020.01.01VermontPlace of death unknown75-84 years0.00.00.00.00.00.0NaN
33849row-spfz-w7in.qeqs00000000-0000-0000-B212-1C12065D8B3801616604699NaN1616604699NaN{ }2021-03-24T00:00:002020-08-01T00:00:002020-08-31T00:00:00By Month2020.08.05WisconsinOther0-17 years0.0NaN0.00.00.00.0One or more data cells have counts between 1-9 and have been suppressed in accordance with NCHS confidentiality standards.
33850row-hx26~enk2_g69b00000000-0000-0000-6674-15DFD35AA38D01616604699NaN1616604699NaN{ }2021-03-24T00:00:002020-08-01T00:00:002020-08-31T00:00:00By Month2020.08.05WisconsinOther18-29 years0.032.00.00.00.00.0NaN